activity
20202026
most citedSpatio-spectral diarization of meetings by combining TDOA-based segmentation and speaker embedding-based clustering

4 citations · 6 across the 9 of their papers we have counts for

collaborators

9 papers

eess.AS2026

On the Role of Spatial Features in Foundation-Model-Based Speaker Diarization

Marc Deegen, Tobias Gburrek, Tobias Cord-Landwehr +4

Recent advances in speaker diarization exploit large pretrained foundation models, such as WavLM, to achieve state-of-the-art performance on multiple datasets. Systems like DiariZe…

eess.AS2025★ 4 cited

Spatio-spectral diarization of meetings by combining TDOA-based segmentation and speaker embedding-based clustering

Tobias Cord-Landwehr, Tobias Gburrek, Marc Deegen +1

We propose a spatio-spectral, combined model-based and data-driven diarization pipeline consisting of TDOA-based segmentation followed by embedding-based clustering. The proposed s…

cs.SD2024

Diminishing Domain Mismatch for DNN-Based Acoustic Distance Estimation via Stochastic Room Reverberation Models

Tobias Gburrek, Adrian Meise, Joerg Schmalenstroeer +1

The room impulse response (RIR) encodes, among others, information about the distance of an acoustic source from the sensors. Deep neural networks (DNNs) have been shown to be able…

eess.AS2023

Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks

Tobias Gburrek, Joerg Schmalenstroeer, Reinhold Haeb-Umbach

We propose a diarization system, that estimates "who spoke when" based on spatial information, to be used as a front-end of a meeting transcription system running on the signals ga…

cs.SD2023

LibriWASN: A Data Set for Meeting Separation, Diarization, and Recognition with Asynchronous Recording Devices

Joerg Schmalenstroeer, Tobias Gburrek, Reinhold Haeb-Umbach

We present LibriWASN, a data set whose design follows closely the LibriCSS meeting recognition data set, with the marked difference that the data is recorded with devices that are…

eess.AS2022★ 2 cited

A Meeting Transcription System for an Ad-Hoc Acoustic Sensor Network

Tobias Gburrek, Christoph Boeddeker, Thilo von Neumann +3

We propose a system that transcribes the conversation of a typical meeting scenario that is captured by a set of initially unsynchronized microphone arrays at unknown positions. It…